The Operating System For
Modern Investment Teams

The Operating System For
Modern Investment Teams

Risk analysis, portfolio construction, performance attribution, strategy development and fund screening — a platform built for professional investors.

Risk analysis, portfolio construction, performance attribution, strategy development and fund screening — a platform built for professional investors.

Risk analysis, portfolio construction, performance attribution, strategy development and fund screening — a platform built for professional investors.

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Platform

MethodTech Products

Risk Model

Decompose portfolio risk into market, style, industry, and stock-specific drivers.

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Risk Model

Decompose portfolio risk into market, style, industry, and stock-specific drivers.

Explore Risk Model

Alpha Machine

Build custom signals, screens, and reusable alpha factors using MethodTech’s data layer and integrated code-editor.

Explore Alpha Machine

Alpha Machine

Build custom signals, screens, and reusable alpha factors using MethodTech’s data layer and integrated code-editor.

Explore Alpha Machine

Portfolio Construction

Build portfolios using constraints, objectives and screening capabilities using a state of the art mean variance optimizer.

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Portfolio Construction

Build portfolios using constraints, objectives and screening capabilities using a state of the art mean variance optimizer.

Explore Portfolio Construction

Strategy Builder

Design, constrain, backtest, and deploy systematic investment strategies.

Explore Strategy Builder

Analytics

Explain returns, risk, drawdowns, factor behaviour, and peer-relative performance.

Explore Analytics

Wealth Management

Screen funds, build mutual fund baskets, and analyse manager skill, risk, and overlap.rols.

Explore Wealth Management

Risk Model

A Risk Model Sits At The Core Of Systematic Investing

A Risk Model Sits At The Core Of Systematic Investing

A statistical framework that converts every position into systematic factor exposures and stock specific (idiosyncratic) risk, letting you gauge future volatility and return drivers before you trade.

A statistical framework that converts every position into systematic factor exposures and stock specific (idiosyncratic) risk, letting you gauge future volatility and return drivers before you trade.

Why

Reveal real drivers of risk to size positions with confidence.

Architecture

Orthogonal factors, full coverage, continuously refreshed.

Efficacies

Demonstrated results across time, markets, and factor styles.

Decomposes each holding into Factor vs Idiosyncratic Risk.

Adjusts weights in real time to hit your target IR, instantly reflected on the dashboard.

Quantify risk in a standardised manner.

Factor Set: 1 Market, 12 Style, 26 Industry factors, built orthogonal to avoid overlap.

Data History: 15+ years of factor data (since 2012) for back-testing and validation.

Update Cycle: Daily refresh of factors & covariance to stay aligned with markets.

Coverage Universe: 100% of listed companies for complete systematic coverage

Proven Explanatory Power: Mean R² 0.45 (Adj 0.42) on daily cross-sectional returns.

Consistent Across Time: Median R² 0.42, with P75 0.56 and P95 0.75 - strong coverage and stability.

Robust Factor Payoffs: Momentum (Sharpe 2.60), Short-Term Reversal (7.48), Low Vol (7.17); HAC-robust t-stats.

Why

Reveal real drivers of risk to size positions with confidence.

Architecture

Orthogonal factors, full coverage, continuously refreshed.

Efficacies

Demonstrated results across time, markets, and factor styles.

Decomposes each holding into Factor vs Idiosyncratic Risk.

Adjusts weights in real time to hit your target IR, instantly reflected on the dashboard.

Quantify risk in a standardised manner.

Factor Set: 1 Market, 12 Style, 26 Industry factors, built orthogonal to avoid overlap.

Data History: 15+ years of factor data (since 2012) for back-testing and validation.

Update Cycle: Daily refresh of factors & covariance to stay aligned with markets.

Coverage Universe: 100% of listed companies for complete systematic coverage

Proven Explanatory Power: Mean R² 0.45 (Adj 0.42) on daily cross-sectional returns.

Consistent Across Time: Median R² 0.42, with P75 0.56 and P95 0.75 - strong coverage and stability.

Robust Factor Payoffs: Momentum (Sharpe 2.60), Short-Term Reversal (7.48), Low Vol (7.17); HAC-robust t-stats.

Use cases

Built for Every Investment Workflow

Quantitative Investments Teams

Fundamental Investment Teams

Wealth Management Teams

ETF & Index Product Teams

Build, test, optimize and deploy systematic strategies on an institutional-grade stack.

Quantitative Investments Teams

Fundamental Investment Teams

Wealth Management Teams

ETF & Index Product Teams

Build, test, optimize and deploy systematic strategies on an institutional-grade stack.

About Us

Meet The Founder

Rachit Parasrampuria, a Computer Science and Economics grad from Cornell University, Rachit led Portfolio Monetization within Citadel’s Equity Quant Research team, building systematic strategies and scalable investment pipelines for multibillion dollar portfolios. 

FAQ

FAQs

What is MethodTech?

Do I need technical team to use the platform?

Who is MethodTech built for?

Can I use specific models on the platform?

Experience MethodTech,
Book a founder demo and
elevate portfolios today.

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